Selecting one of multiple cache eviction algorithms to use to evict a track from the cache by training a machine learning module

ABSTRACT

Provided are a computer program product, system, and method for using a machine learning module to select one of multiple cache eviction algorithms to use to evict a track from the cache. A first cache eviction algorithm determines tracks to evict from the cache. A second cache eviction algorithm determines tracks to evict from the cache, wherein the first and second cache eviction algorithms use different eviction schemes. At least one machine learning module is executed to produce output indicating one of the first cache eviction algorithm and the second cache eviction algorithm to use to select a track to evict from the cache. A track is evicted that is selected by one of the first and second cache eviction algorithms indicated in the output from the at least one machine learning module.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a computer program product, system, andmethod for using a machine learning module to select one of multiplecache eviction algorithms to use to evict a track from the cache.

2. Description of the Related Art

A cache management system buffers tracks in a storage device recentlyaccessed as a result of read and write operations in a faster accessstorage device, such as memory, than the storage device storing therequested tracks. Subsequent read requests to tracks in the fasteraccess cache memory are returned at a faster rate than returning therequested tracks from the slower access storage, thus reducing readlatency.

A cache management system may maintain a linked list having one entryfor each track stored in the cache, which may comprise write databuffered in cache before writing to the storage device or read data. Acache management system uses a cache eviction algorithm to select atrack to evict from cache when space needs to be freed to make room fora track to add to the cache for a read or write request. Cachemanagement algorithms seek to increase a cache hit-to-miss ratio, wherea cache hit occurs when a read request is to a track that is in thecache and may be serviced from the faster access cache and a read missoccurs when the requested track is not in the cache. A read miss resultsin increased latency for a read request to stage the track from thestorage into the cache to return to the request.

Different cache eviction algorithms may result in better cachehit-to-miss ratios in different operating environments and differentread request track patterns.

There is a need in the art for improved techniques for selecting a cacheeviction algorithm to use to evict a track from the cache to improve thecache hit-to-miss ratio in different read access patterns and workloads.

SUMMARY

A first embodiment comprises a computer program product, system, andmethod for using a machine learning module to select one of multiplecache eviction algorithms to use to evict a track from the cache. Afirst cache eviction algorithm determines tracks to evict from thecache. A second cache eviction algorithm determines tracks to evict fromthe cache, wherein the first and second cache eviction algorithms usedifferent eviction schemes. At least one machine learning module isexecuted to produce output indicating one of the first cache evictionalgorithm and the second cache eviction algorithm to use to select atrack to evict from the cache. A track is evicted that is selected byone of the first and second cache eviction algorithms indicated in theoutput from the at least one machine learning module.

Different cache eviction algorithms may improve the cache-hit-to missratio under different operating conditions. Described embodiments use atleast one machine learning module that produces output indicating whichof the first and second cache eviction algorithms to use to select thetrack to evict, which would select the cache eviction algorithm thatwould likely have a greater positive effect on the cache hit-to-missratio.

In a second embodiment, the first embodiment may additionally includeexecuting the first cache eviction algorithm to determine a first evicttrack to evict from the cache and executing the second cache evictionalgorithm to determine a second evict track to evict from the cache. Theexecuted at least one machine learning module receives as input thefirst and the second evict tracks and cache statistics to produce outputused to select one of the first and the second cache eviction algorithmsto use to select a track to evict from the cache.

With the second embodiment, the at least one machine learning algorithmis provided the first and second evict tracks that the first and secondcache eviction algorithms would select to evict and cache statistics touse to determine which of the selected first and second evict tracks toevict would have the most improvement on the cache hit-to-miss ratio.

In a third embodiment, the second embodiment may additionally includethat executing the at least one machine learning module comprisesexecuting a first machine learning module that receives as input thefirst evict track and cache statistics and outputs a first confidencelevel indicating a likelihood that the first cache eviction algorithmoptimizes a read hit rate to the cache and executing a second machinelearning module that receives as input the second evict track and cachestatistics and outputs a second confidence level indicating a likelihoodthat the second cache eviction algorithm optimize the read hit rate tothe cache. The first evict track is evicted from the cache in responseto the first confidence level exceeding the second confidence level andthe second evict track is evicted from the cache in response to thesecond confidence level exceeding the first confidence level.

With the third embodiment, a first and second machine learningalgorithms are trained to estimate a confidence level indicating alikelihood that the cache eviction algorithm associated with the machinelearning algorithm will optimize the read hit rate to the cache. Thetrack selected by the cache eviction algorithm having the highestconfidence level is then evicted from the cache to use the cacheeviction algorithm providing the optimal result for increasing the readhit rate, i.e., minimizing the cache miss rate. In this way, computertechnology for cache eviction is improved by using the cache evictionalgorithm to select a particular track to evict that will have the besteffect on the read hit rate and minimize the cache miss ratio, asindicated by the relative confidence levels produced by the machinelearning algorithms.

A fourth embodiment comprises a computer program product, system, andmethod for demoting tracks from cache to a storage. A first cacheeviction algorithm is executed to determine a first evict track to evictfrom the cache. A second cache eviction algorithm is executed todetermine a second evict track to evict from the cache, wherein thefirst and second cache eviction algorithms use different evictionschemes. At least one machine learning module is executed that receivesas input the first evict track, the second evict track and cachestatistics and produces output used to select one of the first and thesecond cache eviction algorithms to use to select a track to evict fromthe cache. The first evict track is evicted from the cache in responseto the output from the at least one machine learning module indicatingto use the first cache eviction algorithm. The second evict track isevicted from the cache in response to the output from the at least onemachine learning module indicating to use the second cache evictionalgorithm.

The fourth embodiment provides improvements to the computer technologyfor selecting a track to evict by having at least one machine learningmodule consider the tracks that would be selected to evict by differentpossible cache eviction algorithms and cache statistics to determinewhich cache eviction algorithm at that particular moment would select atrack to evict that would be most likely to have a better effect on thecache hit-to-miss ratio, i.e., select a track to evict that would beless likely to be requested in the near future.

In a fifth embodiment, the fourth embodiment additionally includes thatthe output from the at least one machine learning module comprises afirst confidence level indicating a likelihood that the first cacheeviction algorithm optimizes a read hit rate to the cache and a secondconfidence level indicating a likelihood that the second cache evictionalgorithm optimize the read hit rate to the cache. The output indicatesto use the first cache eviction algorithm in response to the firstconfidence level exceeding the second confidence level. The outputindicates to use the second cache eviction algorithm in response to thesecond confidence level exceeding the first confidence level.

With the fifth embodiment at least one machine learning algorithm isused to generate confidence levels for different cache evictionalgorithms that may be used to select a track to evict. The confidencelevels indicate which cache eviction algorithm at the moment, based onthe track it suggests to evict and current cache statistics, wouldlikely optimize the cache read hit rate and be the best cache evictionalgorithm to improve cache performance.

In a sixth embodiment, the fourth embodiment additionally includes thatthe at least one machine learning module comprises a first machinelearning module that receives as input the first evict track and outputsthe first confidence level and a second machine learning module thatreceives as input the second evict track and outputs the secondconfidence level. An active cache list indicates the tracks in the cacheand an inactive cache list indicates tracks evicted from the cache.Indication of a track is removed from the active cache list when thetrack is evicted from the cache. Indication of the track evicted fromthe cache is added to the inactive cache list. In response to a readrequest to a requested track not indicated in the active cache list andindicated in the inactive cache list, a modified first confidence levelis determined based on first information on the requested track in theinactive cache list. A modified second confidence level is determinedbased on second information on the requested track in the inactive cachelist. The first machine learning module is retrained to produce themodified first confidence level for the requested track. The secondmachine learning module is retrained to produce the modified secondconfidence level for the requested track.

With the sixth embodiment, an inactive cache list is used to maintaininformation on tracks evicted from cache. If there is a read miss, thena modified first confidence level and second confidence level aredetermined based on first information and second information on therequested track in the inactive cache list having information on evictedtracks. For instance, if the information for the requested track in theinactive cache list indicates the track is more likely to be selected bythe cache eviction algorithm, then the confidence level produced by themachine learning module for that cache eviction algorithm for thatrequested track and cache statistics is increased to increase thelikelihood that that cache eviction algorithm is selected because it ismore likely to select the requested track. On the other hand, if theinformation for the requested track in the inactive cache list indicatesthe track is less likely to be selected by the cache eviction algorithm,then the confidence level produced by the machine learning module forthat requested track and cache statistics is reduced to reduce thelikelihood that that cache eviction algorithm is selected because it isless likely to select the requested track. In this way, the machinelearning modules are retrained based on a requested track resulting in acache miss to produce a confidence level more likely to select a cacheeviction algorithm that would likely select the requested track andproduce a confidence level less likely to select a cache evictionalgorithm that would be less likely to select the requested track. Inthis way, the confidence levels are adjusted to increase the likelihoodthat the cache eviction algorithm that would select the requested trackresulting in the cache miss is used so as to reduce cache misses in thefuture and improve the hit-to-miss ratio.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a computing environment.

FIG. 2 illustrates an embodiment of a Least Recently Used (LRU) list.

FIG. 3 illustrates an embodiment of a cache control block.

FIG. 4 illustrates an embodiment of cache statistics.

FIGS. 5a and 5b illustrate an embodiment of operations to process a readrequest to a track.

FIG. 6 illustrates an embodiment of operations to retrain machinelearning algorithms for a read miss.

FIG. 7 illustrates an embodiment of operations to determine a modifiedconfidence level for a LRU algorithm that should be output for therequested track if there is a read miss to the requested track.

FIG. 8 illustrates an embodiment of operations to determine a modifiedconfidence level for a Least Frequently Used (LFU) algorithm that shouldbe output for the requested track if there is a read miss to therequested track.

FIG. 9 illustrates a computing environment in which the components ofFIG. 1 may be implemented.

DETAILED DESCRIPTION

A storage controller demotes tracks from an active cache to make room tocache data for new I/O requests, e.g., read or write requests. If thedata for a read request is already in the cache, i.e., a cache hit, thenthe requested data does not need to be staged into the cache. If thedata for the read request is not in the cache, i.e., a cache miss, or ifthere is write data for the request, then tracks in the cache may needto be evicted from the cache to make space for the read data to stageinto cache or the write data. Tracks are evicted based on cache evictionalgorithms. If the cache miss rate is high, data needs to be continuallystaged from storage into the cache, and then new I/O requests will needto be queued until space is freed in the cache. Such queuing of I/Orequests can cause severe performance problems and high response timesto the queued I/O requests.

Described embodiments provide improvements to computer technology forevicting a track from a cache by providing a first and second cacheeviction algorithms and executing at least one machine learning moduleto produce output indicating one of the first cache eviction algorithmand the second cache eviction algorithm to use to select a track toevict from the cache to improve the cache hit-to-miss ratio that willmaximize the cache hit-to-miss ratio, and minimize read latency.Described embodiments provide improvements for selecting a cacheeviction algorithm to use to determine the track to evict by usingmachine learning modules to process the requested track and determinethe cache eviction algorithm that will best optimize the cachehit-to-miss ratio by using machine learning optimization techniques. Inthis way, described embodiments improve the selection of a cacheeviction algorithm to use to evict a track by considering which cacheeviction algorithm has the highest confidence value, or more likely toincrease the hit-to-miss ratio and reduce the number of cache misses.

FIG. 1 illustrates an embodiment of a computing environment. A computingsystem 100 accesses data in volumes 102 (e.g., Logical Unit Numbers,Logical Devices, Logical Subsystems, etc.) configured in a storage 104.The computing system 100 includes a processor 106 and a memory 108,including an active cache 110 to cache data for the storage 104. Theprocessor 106 may comprise one or more central processing units (CPUs)or a group of multiple cores on a single CPU. The active cache 110buffers data requested by processes within the computing system.Alternatively, the computing system 100 may comprise a storagecontroller that processes Input/Output (I/O) access requests for tracksin the storage 104 from hosts 118 connecting to the computing system 100(storage controller) over a network 120.

The memory 108 further includes a storage manager 122 and cache manager124. The storage manager 122 manages access requests from internalprocesses in the computing system 100 and/or from hosts 118 for tracksin the storage 104. The cache manager 124 maintains accessed tracks inthe active cache 110 for future read access to the tracks to allow theaccessed tracks to be returned from the faster access cache 110 insteadof having to retrieve from the storage 104. A track may comprise anyunit of data configured in the storage 104, such as a track, LogicalBlock Address (LBA), etc., which is part of a larger grouping of tracks,such as a volume, logical device, etc.

The cache manager 124 maintains cache management information 126 in thememory 108 to manage read (unmodified) and write (modified) tracks inthe cache 110. The cache management information 126 may include a trackindex 128 providing an index of tracks in the cache 110 to cache controlblocks in a control block directory 300; an active cache Least RecentlyUsed (LRU) list 200 _(A) for tracks in the active cache 110 and aninactive cache LRU list 200 _(IA) to indicate tracks that have beenevicted from the active cache 110 for purposes of tracking cache accessto optimize cache eviction algorithms. The control block directory 300includes the cache control blocks, where there is one cache controlblock for each track in the cache 110 providing metadata on the track inthe cache 110. The track index 128 associates tracks with the cachecontrol blocks providing information on the tracks in the cache 110.Upon determining that the active cache LRU list 200 _(A) is full or hasreached a threshold level, the active cache LRU list 200 _(A) is used todetermine tracks to evict from the cache.

In one embodiment, cache control blocks are maintained for tracksindicated in the active cache LRU list 200 _(A) and in the inactivecache LRU list 200 _(IA), with the difference being the track data fortracks indicated in the inactive cache LRU list 200 _(IA) may not bestored in the cache 110 as they have been evicted from cache 110. In astill further alternative embodiment, the cache control blocks used fortracks in the inactive cache LRU list 200 _(IA) may have fewer fieldsand less information than those for tracks maintained in the cache 110,such that a cache control block for a track indicated on the inactiveLRU cache list 200 _(IA) may require less data. In a further embodiment,data for tracks in the inactive cache LRU list 200 _(IA) may be storedin a ghost cache.

The memory 108 further includes a first cache eviction algorithm 130 ₁and a second cache eviction algorithm 130 ₂ that provide differentalgorithms for selecting tracks to evict. For instance, in oneembodiment the first cache eviction algorithm 130 ₁ may comprise a LeastRecently Used (LRU) algorithm that selects a track at the LRU end of theactive cache LRU list 200 _(A) to evict, or cache that is the leastrecently accessed in the cache 110 to evict. The active cache LRU list200 _(A) may comprise a double linked list to track the access order. Ifa track is added to the cache 110, then an entry for the added track isadded to the most recently used (MRU) end of the active cache LRU list200 _(A). If a track is accessed or updated that is already in the cache110, then the entry for that track in the active cache LRU list 200 _(A)is moved to the MRU end of the list 200 _(A). When the cache 110 reachesits maximum size the track indicated at the LRU end, least recently usedor accessed, will be evicted from the cache 110.

The second cache eviction algorithm 130 ₂ may comprise a leastfrequently used (LFU) algorithm that evicts a track that is leastfrequently used or fewest number of accesses while in cache 110, wherethe cache control blocks 300 for the tracks indicate a frequency counterindicating a number of times the track was accessed while in the activecache 110. In further embodiments, the first 130 ₁ and/or second 130 ₂cache eviction algorithm may implement alternative cache evictiontechniques, such as adaptive replacement cache (ARC), clock withadaptive replacement (CAR), LFU with dynamic aging (LFUDA), lowinter-reference recency set (LIRS), time aware least recently used(TLRU), most recently used (MRU), pseudo-LRU (PLRU), random replacement(RR), segmented LRU (SLRU), Bélády's algorithm, First in first out(FIFO), Last in first out (LIFO), Multi-queue (MQ), etc.

The memory 108 may further implement a first machine learning module 132₁ and a second machine learning module 132 ₂, which implement a machinelearning technique such as decision tree learning, association rulelearning, neural network, inductive programming logic, support vectormachines, Bayesian models, etc., to determine a confidence levelindicating a likelihood that the eviction algorithm associated with themachine learning algorithm selects a track to evict that optimizes thehit-to-miss ratio to increase the likelihood that a requested track toaccess is in the active cache 110. For instance, the first machinelearning module 132 ₁ may receive as input the track determined by thefirst cache eviction algorithm 130 ₁ to evict and cache statistics toproduce a first confidence level that the first cache eviction algorithm130 ₁, such as an LRU eviction algorithm, will select a track to evictthat maximizes the hit-to-miss ratio. The second machine learning module132 ₂ may receive as input the track determined by the second cacheeviction algorithm 130 ₂ to evict and cache statistics to produce asecond confidence level that the second cache eviction algorithm 130 ₂,such as an LFU eviction algorithm, will select a track to evict thatmaximizes the hit-to-miss ratio. The cache manager 124 may then use theoutputted first and second confidence levels to select the selectedtrack to evict from the first 130 ₁ or the second 130 ₂ cache evictionalgorithm.

In one embodiment, the first 132 ₁ and the second 132 ₂ machine learningalgorithms may comprise separate artificial neural network programs. Thefirst neural network 132 ₁may be trained using backward propagation toadjust weights and biases at nodes in a hidden layer of the firstartificial neural network program to produce a desired first confidencelevel based on input comprising a track to evict from the first evictcache algorithm 130 ₁ and the cache statistics. The second neuralnetwork 132 ₂ may be trained using backward propagation to adjustweights and biases of nodes in a hidden layer of the second artificialneural network program to produce a desired second confidence levelbased on input comprising a track to evict from the second evict cachealgorithm 130 ₂ and the cache statistics. In back propagation, themargin of error of the output is measured and the weights and biases atnodes in the hidden layer are adjusted accordingly to decrease theerror. Back propagation may comprise an algorithm for supervisedlearning of artificial neural networks using gradient descent. Given anartificial neural network and an error function, the method maycalculate the gradient of the error function with respect to the neuralnetwork's weights and biases.

During caching operations, the cache manager 124 may gather for specificintervals of time, such as 15 second intervals, cache access statistics400, such as cache hit-to-miss ratio for the currently used cache 110and a cache size.

Although FIG. 1 shows two cache eviction algorithms and two machinelearning modules, in further embodiments there may be more than twocache eviction algorithms and two machine learning modules. Further,instead of a separate machine learning module 132 _(i) to determine aconfidence level for each of the cache eviction algorithms 130 _(i), onemachine learning module 132 may be used to select a cache evictionalgorithm to use based on input of the track to evict selected by thecache eviction algorithms 130 ₁, 130 ₂ and cache statistics 400.

In the described embodiments, the lists 200 _(A) and 200 _(IA) compriseLRU lists. In alternative embodiments, the lists 200 _(A) and 200 _(IA)may comprise other types of lists to organize indication of tracks inthe cache 110.

The storage manager 122, cache manager 124, the first cache evictionalgorithm 130 ₁, the second cache eviction algorithm 130 ₂, the firstmachine learning module 132 ₁, and the second machine learning module132 ₂ are shown in FIG. 1 as program code loaded into the memory 108 andexecuted by the processor 106. Alternatively, some or all of thefunctions may be implemented in hardware devices in the system 100, suchas in Application Specific Integrated Circuits (ASICs) or executed byseparate dedicated processors.

The storage 104 may comprise one or more storage devices known in theart, such as a solid state storage device (SSD) comprised of solid stateelectronics, EEPROM (Electrically Erasable Programmable Read-OnlyMemory), flash memory, flash disk, Random Access Memory (RAM) drive,storage-class memory (SCM), Phase Change Memory (PCM), resistive randomaccess memory (RRAM), spin transfer torque memory (STM-RAM), conductivebridging RAM (CBRAM), magnetic hard disk drive, optical disk, tape, etc.The storage devices may further be configured into an array of devices,such as Just a Bunch of Disks (JBOD), Direct Access Storage Device(DASD), Redundant Array of Independent Disks (RAID) array,virtualization device, etc. Further, the storage devices may compriseheterogeneous storage devices from different vendors or from the samevendor.

The memory 108 may comprise a suitable volatile or non-volatile memorydevices, including those described above.

In certain embodiments, the active cache 110 comprises a cache to storetracks immediately from the storage 104. In alternative embodiments, thedescribed cache eviction techniques herein using multiple cache evictionalgorithms 130 ₁, 130 ₂ and machine learning modules 132 ₁, 132 ₂ may beused to evict tracks from processor caches, such as L1 and L2 caches, alast level cache, and other caches in the system 100.

The network 120 may comprise a Storage Area Network (SAN), a Local AreaNetwork (LAN), a Wide Area Network (WAN), the Internet, and Intranet,etc.

FIG. 2 illustrates an embodiment of one of the LRU lists 200 _(i), suchas LRU cache lists 200 _(A) and 200 _(IA), as having a most recentlyused (MRU) end 202 identifying a track most recently added to the cache110 or most recently accessed in the cache 110 and a least recently used(LRU) end 204 from which the track identified at the LRU end 204 isselected to demote from the cache 110. The LRU end 204 points to a trackidentifier, such as a track identifier address or a cache control blockindex for the track, of the track that has been in the cache 110 thelongest for tracks indicated in that list 200 _(A). The inactive cacheLRU list 200 _(IA) indicates tracks demoted from the cache 110.

FIG. 3 illustrates an embodiment of a cache control block 300 _(i) forone of the tracks in the active cache 110, including, but not limitedto, a cache control block identifier 302, such as an index value of thecache control block 300 _(i); a track ID 304 of the track in the storage104; the cache LRU list 306 in which the cache control block 300 _(i) isindicated; an LRU list entry 308 at which the track is indicated; acache timestamp 310 indicating a time the track was added to the cache110 and indicated on the LRU list 304; and a frequency counter 312indicating a number of times the track 304 was accessed while the trackwas in the active cache 110. While on the inactive cache LRU list 200_(IA), the frequency counter 312 will not be updated, unless the trackindicated in the inactive cache LRU list 200 _(IA) is moved back to theactive cache LRU list 200 _(A).

FIG. 4 illustrates an embodiment of time interval cache statistics 400gathered for one time interval. The cache manager 124 may gather cacheaccess statistics for time intervals within a large time period duringcaching operations. The time interval cache access statistics 400_(i)include a cache hit-to-miss ratio 402 indicating the number of hitsas a ratio of hits and misses and a cache size 404 of an amount of cacheconsumed by tracks. Other statistics may also be gathered.

FIGS. 5a and 5b illustrate an embodiment of operations performed by thecache manager 124 to use the machine learning modules 132 ₁ and 132 ₂ todetermine a cache eviction algorithm 130 ₁ and 130 ₂ to use to select atrack to evict from the cache 110 to make room in cache 110 for a trackto stage from the storage 104 to return to a read request from a host118. Upon receiving (at block 500) the read request, if (at block 502)the requested track to read is in the active cache LRU list 200 _(A),then the entry 200 _(RT) for the requested track (RT) is moved (at block504) to the MRU end 202 of the active cache LRU list 200 _(A). The cachecontrol block 300 _(RT) for the requested track is updated (at block506) to increment the frequency counter 312 and update the LRU listentry 308 to indicate the MRU end 202. The cached requested track isreturned (at block 508) from the cache 110 to the host 118 requestingthe track.

If (at block 502) the track is not in the cache 110 and if (at block510) there is not sufficient space in the cache 110 for the requestedtrack then control proceeds to block 512 to initiate the process toselect a track to evict by the first 130 ₁ or second 130 ₂ cacheeviction algorithms. At block 512, the cache manager 124 invokesexecution of the first cache eviction algorithm 130 ₁ (e.g., LRU) todetermine a first track to evict and invokes execution (at block 514) ofthe second cache eviction algorithm 130 ₂ (e.g., LFU) to determine asecond track to evict.

The cache manager 124 may then invoke to execute (at block 516) thefirst machine learning module 132 ₁ that receives as input the firstevict track, i.e., first track to evict, and cache statistics 400 andoutputs a first confidence level indicating a likelihood that the firstcache eviction algorithm 130 ₁ optimizes a read hit-to-miss rate at thecache 110. The cache manager 124 invokes to execute (at block 518) thesecond machine learning module 132 ₂ that receives as input the secondevict track, i.e., second track to evict, and cache statistics 400 andoutputs a second confidence level indicating a likelihood that thesecond cache eviction algorithm 130 ₂ optimizes a read hit rate to thecache 110. If (at block 520) the first confidence level is greater thanthe second confidence level, then the cache manager 124 evicts (at block522) the first evict track from the cache 110, selected by the firstmachine learning module 132 ₁. Otherwise, if (at block 520) the secondconfidence level is greater, the second evict track is evicted (at block524) from the cache 110, as selected by the second machine learningmodule 132 ₂.

The operations at blocks 512-524 to evict a track from cache 110 mayalso be used to free room in the cache 110 for a write request to atrack not already in the cache.

With respect to FIG. 5b , after the track is evicted (at block 522 or524), if (at block 526) the requested track is indicated in the inactiveLRU cache list 200 _(IA), then the entry for the requested track 200_(RT) in the inactive cache LRU list 200 _(IA) is moved (at block 528)to the MRU end 202 of the active cache LRU list 200 _(A). The cachecontrol block 300 _(RT) for the requested track is updated (at block530) to increment the frequency counter 312, update the cache LRU list306 to indicate active LRU cache list 200 _(A), and update the LRU listentry 308 to indicate the MRU end 202.

If (at block 526) the requested track is not in inactive cache LRU list200 _(IA), then the cache manager 124 adds (at block 532) an entry tothe MRU end 202 of the active cache LRU list 200 _(A) for the requestedtrack and creates (at block 534) a cache control block 300 _(RT) for therequested track, with a frequency counter 312 of one, and indicate theactive cache LRU list 200 _(A) and MRU end 202 in fields 306 and 308,respectively, of the cache control block 300 _(RT). If (at block 536)the inactive cache LRU list 200 _(IA) is full, then an entry at the LRUend 204 of the inactive cache LRU list 200 _(IA) is removed (at block538). If (at block 536) the inactive cache LRU list 200 _(IA) is notfull or after the entry is removed (at block 538), the cache manager 124indicates (at block 540) the evicted track at the MRU end 202 of theinactive cache LRU list 200 _(IA) and updates the cache control block300 _(ET) for the evicted track (ET) to indicate the inactive cache LRUlist 200 _(IA) and MRU end 202 in fields 306 and 308, respectively, ofthe cache control block 300 _(ET).

From block 540 or 530, the requested track is staged into the cache 110and returned (at block 542) to the requesting host 118. Control thenproceeds (at block 544) to FIG. 6 to retrain the machine learningalgorithms 132 ₁, 132 ₂ to adjust for the read miss to the requestedtrack.

If (at block 510) the requested track is not in the cache 110 and thereis space in the cache 110 for the requested track, then the cachemanager 124 performs (at block 546) the operations at blocks 526 to 534to have the requested track indicated at the MRU end 202 of the activeLRU cache list 200 _(A) for situations where the requested track is oris not in the inactive LRU cache list 200 _(IA). The cache manager 124then proceeds (at block 548) to block 542 in FIG. 5b to stage therequested track into the cache 110 and retrain the machine learningmodules 132 ₁, 132 ₂ to address the cache miss.

With the embodiments of FIGS. 5a and 5b , machine learning algorithmsare used to determine confidence levels for the different cache evictionalgorithms so that the cache eviction algorithm having the highestconfidence level for selecting a track to evict is used to select thetrack to evict, where the confidence level indicates the likelihood thecache eviction algorithm is selecting a track to evict to optimize thecache hit-to-miss ratio that will minimize cache misses, or maximizecache hits.

FIG. 6 illustrates an embodiment of operations performed by the machinelearning modules 132 ₁ and 132 ₂, or another component such as the cachemanager 124, to retrain the machine learning modules 132 ₁, 132 ₂predictive algorithms if there is a read miss to a requested track. Uponinitiating (at block 600) the operation to retrain the machine learningalgorithms 132 ₁, 132 ₂, the first machine learning module 132 ₁determines (at block 602) a modified first confidence level based onfirst information on the requested track in the inactive cache list,e.g., based on a position of the requested track in the inactive cachelist. In one embodiment, where the first cache eviction algorithm 130 ₁comprises an LRU eviction algorithm, the first confidence level isincreased in response to the requested track being closer to the MRU end202 of the inactive LRU cache list 200 _(IA) than the LRU end 204, whichmeans the cache miss was to a track that was relatively high in theinactive cache LRU list 200 _(IA). If the requested track is closer tothe LRU end 204 than the MRU end 202, then the first confidence levelmay be decreased, because the track accessed at a level below average ofthe tracks recorded in the inactive LRU cache list 200 _(IA).

The second machine learning module 132 ₂ determines (at block 604) amodified second confidence level based on second information on therequested track in the inactive cache list, e.g., based on a frequencycounter 312 of the requested track in the inactive cache list. In anembodiment where the second cache eviction algorithm 130 ₂ comprises aLFU algorithm to evict a least frequently used track based on thefrequency counter 312, the second confidence level is increased inresponse to the frequency counter value of the requested track beinggreater than the average of the frequency counter values of the tracksin the inactive cache list, which means the LFU eviction algorithm wouldproduce a better result given the requested track has a greater thanaverage frequency counter value. The second confidence level may bedecreased in response to the frequency counter value of the requestedtrack being less than the average of the frequency counter values of thetracks in the inactive cache list, because in such case the LFU evictionalgorithm would not produce a good eviction result for the requestedtrack, which has a less than average frequency counter 312, meaning therequested track is less likely to be a qualified track for selectionbased on the frequency of access predictor.

After determining a modified first and second confidence levels, themachine learning modules 132 ₁, 132 ₂ may then use those values toretrain the first 132 ₁ and second 132 ₂ machine learning modulealgorithms to produce the modified first and second confidence levels,respectively, for the requested track that resulted in a cache miss. Thefirst machine learning module 132 ₁ may retrain (at block 606) the firstmachine learning module 132 ₁ algorithm, such as the weights and biasesat the nodes of the hidden layer using back propagation to reduce themargin of error, to produce the modified first confidence level for therequested track using as input the requested track and cache statistics400. The second machine learning module 132 ₂ further retrains (at block608) the second machine learning module 132 ₂ to produce the modifiedsecond confidence level for the requested track using as input therequested track and cache statistics.

In embodiments where the machine learning modules 132 ₁ and/or 132 ₂predictive algorithms comprise neural networks, the cache manager 124may invoke a backward propagation routine to retrain the machinelearning modules to produce the modified first and second confidencelevels. For other types of machine learning algorithms, such as Bayesianmodels, other techniques may be used to retrain the machine learningmodules 132 ₁, 132 ₂ to produce the modified confidence levels base4d onthe input.

With the embodiments of FIG. 6, the machine learning modules 132 ₁, 132₂ are retrained in response to a read miss by adjusting the confidencelevels produced for the missed track using information on access to therequested track maintained in the inactive LRU cache list 200 _(IA),including a position of the requested track in the inactive LRU cachelist 200 _(IA) and a frequency counter value for the requested track.This allows for real time adjustment to improve the predictability ofthe machine learning algorithms 132 ₁, 132 ₂ based on information on animmediate cache miss for a requested track to improve the predictabilityof the machine learning algorithms in selecting a cache evictionalgorithm to evict a track from the track to make room for the requestedtrack that resulted in the miss.

FIG. 7 illustrates an embodiment of operations performed by the firstmachine learning module 132 ₁, or other component such as the cachemanager 124, to calculate the modified first confidence level when thefirst eviction algorithm comprises an LRU algorithm, such as performedat block 606 in FIG. 6. Upon initiating the operation to calculate (atblock 700) the modified first confidence level, if (at block 702) therequested track is closer to the MRU end 202 than the LRU end 204, i.e.,has a better than average recent access, then the first machine learningmodule 132 ₁ calculates (at block 704) a positive margin of error asequal to:

(position of requested track in inactive cache list minus (an inactivecache list size divided by two)) divided by (the inactive cache listsize divided by two).

The first machine learning module 132 ₁ increases (at block 706) thefirst confidence level by the positive margin to produce a highermodified first confidence level, e.g., multiply first confidence levelby (1 plus positive margin of error).

If (at block 702) the requested track is closer to the LRU end 204, thenthe first machine learning module 132 ₁ calculates (at block 708) anegative margin of error as equal to:

((an inactive cache list size divided by two) minus the position of therequested track in the inactive cache list minus) divided by (theinactive cache list size divided by two)

The first machine learning module 132 ₁ reduces (at block 710) the firstconfidence level by the negative margin to produce a lower modifiedfirst confidence level, e.g., multiply first confidence level by (1minus negative margin of error).

With the embodiment of operations of FIG. 7, the position of therequested track in the inactive LRU cache list 200 _(IA) is used todetermine a margin of error for the first confidence level based on anextent to which the requested track resulting in the cache miss is inthe upper or lower half of the inactive LRU cache list 200 _(IA). If therequested track is in the upper half of the inactive LRU cache list 200_(IA), then the requested track was accessed more recently than theaverage track in the inactive LRU cache list 200 _(IA), and thus the LRUeviction algorithm should produce a higher confidence level for therequested track, i.e., more recently accessed means more likely to haveread hits. If the requested track is in the lower half of the inactiveLRU cache list 200 _(IA), then the requested track was accessed lessrecently than the average track in the inactive LRU cache list 200_(IA), and thus the LRU eviction algorithm should produce a lowerconfidence level for the less recently accessed requested track, whichis not likely to reduce cache misses.

FIG. 8 illustrates an embodiment of operations performed by the secondmachine learning module 132 ₂, or other component such as the cachemanager 124, to calculate the modified second confidence level when thesecond eviction algorithm 130 ₂ comprises an LFU algorithm, such asperformed at block 608 in FIG. 6. Upon initiating the operation tocalculate (at block 800) the modified second confidence level, if (atblock 802) the requested track frequency counter 312 is greater than anaverage of the frequency counter values 312 for the tracks in theinactive cache LRU list 200 _(IA), then the second machine learningmodule 132 ₂ calculates (at block 804) a positive margin of error asequal to:

(the frequency count value of the requested track minus the average ofthe frequency counter values) divided by (a maximum frequency countervalue of frequency counter values in the inactive cache list minus theaverage of the frequency counter values)

The second machine learning module 132 ₂ increases (at block 806) thesecond confidence level by the positive margin to produce a highermodified second confidence level, e.g., multiply second confidence levelby (1 plus positive margin of error).

If (at block 802) the requested track frequency counter 312 is less thanan average of the frequency counter values 312 for the tracks in theinactive cache LRU list 200 _(IA), then the second machine learningmodule 132 ₂ calculates (at block 808) a negative margin of error asequal to:

(the average of the frequency counter values minus the frequency countvalue of the requested track) divided by (a maximum frequency countervalue of frequency counter values in the inactive cache list minus theaverage of the frequency counter values).

The second machine learning module 132 ₂ decreases (at block 810) thesecond confidence level by the negative margin to produce a lowermodified second confidence level, e.g., multiply second confidence levelby (1 minus negative margin of error).

With the embodiment of operations of FIG. 8, the position of the accessfrequency of the requested track in the inactive LRU cache list 200_(IA) is used to determine a margin of error for the second confidencelevel based on an extent to which the requested track resulting in thecache miss has a greater or less than average access frequency. If therequested track has a greater than average access frequency in theinactive LRU cache list 200 _(IA), then the requested track was accessedmore frequency than most tracks in the inactive LRU cache list 200_(IA), and thus the LFU eviction algorithm should produce a higherconfidence level for the requested track, i.e., more read hits. If therequested track has a lower than average access frequency of the tracksin the inactive LRU cache list 200 _(IA), then the requested track wasaccessed less frequently than the average track in the inactive LRUcache list 200 _(IA), and thus the LFU eviction algorithm should producea lower confidence level for the less frequently accessed requestedtrack and not contribute to a better cache hit-to-miss ratio.

In the described embodiment, variables “i”, etc., when used withdifferent elements may denote a same or different instance of thatelement.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computational components of FIG. 1, including the hosts 118 andcomputing system 100 may be implemented in one or more computer systems,such as the computer system 902 shown in FIG. 9. Computer system/server902 may be described in the general context of computer systemexecutable instructions, such as program modules, being executed by acomputer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system/server 902 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 9, the computer system/server 902 is shown in the formof a general-purpose computing device. The components of computersystem/server 902 may include, but are not limited to, one or moreprocessors or processing units 904, a system memory 906, and a bus 908that couples various system components including system memory 906 toprocessor 904. Bus 908 represents one or more of any of several types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 902 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 902, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 906 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 910 and/or cachememory 912. Computer system/server 902 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 913 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 908 by one or more datamedia interfaces. As will be further depicted and described below,memory 906 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 914, having a set (at least one) of program modules 916,may be stored in memory 906 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. The components of the computer 902 may be implemented asprogram modules 916 which generally carry out the functions and/ormethodologies of embodiments of the invention as described herein. Thesystems of FIG. 1 may be implemented in one or more computer systems902, where if they are implemented in multiple computer systems 902,then the computer systems may communicate over a network.

Computer system/server 902 may also communicate with one or moreexternal devices 918 such as a keyboard, a pointing device, a display920, etc.; one or more devices that enable a user to interact withcomputer system/server 902; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 902 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 922. Still yet, computer system/server 902can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 924. As depicted, network adapter 924communicates with the other components of computer system/server 902 viabus 908. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 902. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

The foregoing description of various embodiments of the invention hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe invention. Since many embodiments of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims herein after appended.

1-28. (canceled)
 29. A computer program product for demoting tracks fromcache to a storage, the computer program product comprising a computerreadable storage medium having computer readable program code embodiedtherein that when executed performs operations, the operationscomprising: providing a first machine learning module that receives asinput first evict track to evict from the cache determined by a firstcache eviction algorithm and outputs a confidence level indicating alikelihood the first cache eviction algorithm optimizes a read hit rateto the cache; providing a second machine learning module that receivesas input a second evict track to evict from the cache determined by asecond cache eviction algorithm and outputs a confidence levelindicating a likelihood the second cache eviction algorithm optimizes aread hit rate to the, wherein the first and second cache evictionalgorithms use different eviction schemes; determining a modified firstconfidence level based on first information on a specified track;determining a modified second confidence level based on secondinformation on the specified track; retraining the first machinelearning module to produce the modified first confidence level for thespecified track; and retraining the second machine learning module toproduce the modified second confidence level for the specified track.30. The computer program product of claim 29, wherein the first andsecond machine learning modules comprise first and second artificialneural network programs, respectively, wherein the retraining the firstmachine learning module comprises using backward propagation to adjustweights and biases for of nodes in a hidden layer of the firstartificial neural network program to produce the modified firstconfidence level based on input comprising the specified track and cachestatistics; and wherein the retraining the second machine learningmodule comprises using backward propagation to adjust weights and biasesof nodes in a hidden layer of the second artificial neural networkprogram to produce the modified second confidence level based on inputcomprising the specified track and the cache statistics.
 31. Thecomputer program product of claim 29, wherein the specified trackcomprises a requested track subject to a read request determined not tobe in the cache, wherein the operations further comprise: maintaining anactive cache list of the tracks in the cache; maintaining an inactivecache list of tracks evicted from the cache and removed from the activecache list; wherein the determining the modified first and secondconfidence levels and the retraining the first and second machinelearning modules are performed in response to the requested track notindicated in the active cache list and indicated in the inactive cachelist.
 32. The computer program product of claim 31, wherein the activeand inactive cache lists comprise least recently used (LRU) lists oftracks in the cache, wherein the first cache eviction algorithmcomprises an LRU algorithm that selects a track to evict that is at anLRU end of the LRU list, wherein the determining the modified firstconfidence level is based on a position of the requested track in theinactive cache list.
 33. The computer program product of claim 32,wherein the determining the modified first confidence level based on theposition of the requested track in the inactive cache list comprises:increasing the first confidence level in response to the requested trackbeing closer to a most recently used (MRU) end of the inactive cachelist than the LRU end; and decreasing the first confidence level inresponse to the requested track being closer to the LRU end than the MRUend.
 34. The computer program product of claim 32, wherein thedetermining the modified first confidence level based on the position ofthe requested track in the inactive cache list comprises: in response tothe requested track being closer to a most recently used end of theinactive cache list than the LRU end, performing: calculating a positivemargin of error comprising (the position of the requested track in theinactive cache list minus (an inactive cache list size divided by two))divided by the inactive cache list size divided by two; and adjustingthe first confidence level by the positive margin of error to producethe modified first confidence level; in response to the requested trackbeing closer to the LRU end than a most recently used end of theinactive cache list, performing: calculating a negative margin of errorcomprising ((an inactive cache list size divided by two) minus theposition of the requested track in the inactive cache list minus)divided by the inactive cache list size divided by two; and adjustingthe first confidence level by the negative margin of error to producethe modified first confidence level.
 35. The computer program product ofclaim 31, wherein the operations further comprise: providing a frequencycounter for each of the tracks in the active and inactive cache listsindicating a number of times a track in the cache has been accessedwhile on the active cache list before being moved to the inactive cachelist, wherein the second cache eviction algorithm comprises a LeastFrequently Used (LFU) algorithm that selects a track to evict that has alowest frequency counter of the frequency counters for the tracks in thecache, wherein the determining the modified second confidence level isbased on a frequency counter value of the requested track with respectto an average of frequency counter values of tracks in the inactivecache list.
 36. The computer program product of claim 35, wherein thedetermining the modified second confidence level based on the frequencycounter value of the requested track in the inactive cache listcomprises: increasing the second confidence level in response to thefrequency counter value of the requested track being greater than theaverage of the frequency counter values of the tracks in the inactivecache list; and decreasing the second confidence level in response tothe frequency counter value of the requested track being less than theaverage of the frequency counter values of the tracks in the inactivecache list.
 37. The computer program product of claim 35, wherein thedetermining the modified second confidence level based on the frequencycounter value of the requested track in the inactive cache listcomprises: in response to the frequency counter value of the requestedtrack being greater than the average of the frequency counter values ofthe tracks in the inactive cache list, performing: calculating apositive margin of error comprising (the frequency count value of therequested track minus the average of the frequency counter values)divided by (a maximum frequency counter value of frequency countervalues in the inactive cache list minus the average of the frequencycounter values); and adjusting the second confidence level by thepositive margin of error to produce the modified second confidencelevel; in response to the frequency counter value of the requested trackbeing less than the average of the frequency counter values of thetracks in the inactive cache list, performing: calculating a negativepositive margin of error comprising (the average of the frequencycounter values minus the frequency count value of the requested track)divided by (a maximum frequency counter value of frequency countervalues in the inactive cache list minus the average of the frequencycounter values); and adjusting the second confidence level by thenegative margin of error to produce the modified second confidencelevel.
 38. A system for demoting tracks from cache to a storage,comprising: a processor; and a computer readable storage medium havingcomputer readable program code embodied therein that when executedperforms operations, the operations comprising: providing a firstmachine learning module that receives as input first evict track toevict from the cache determined by a first cache eviction algorithm andoutputs a confidence level indicating a likelihood the first cacheeviction algorithm optimizes a read hit rate to the cache; providing asecond machine learning module that receives as input a second evicttrack to evict from the cache determined by a second cache evictionalgorithm and outputs a confidence level indicating a likelihood thesecond cache eviction algorithm optimizes a read hit rate to the,wherein the first and second cache eviction algorithms use differenteviction schemes; determining a modified first confidence level based onfirst information on a specified track; determining a modified secondconfidence level based on second information on the specified track;retraining the first machine learning module to produce the modifiedfirst confidence level for the specified track; and retraining thesecond machine learning module to produce the modified second confidencelevel for the specified track.
 39. The system of claim 38, wherein thespecified track comprises a requested track subject to a read requestdetermined not to be in the cache, wherein the operations furthercomprise: maintaining an active cache list of the tracks in the cache;maintaining an inactive cache list of tracks evicted from the cache andremoved from the active cache list; wherein the determining the modifiedfirst and second confidence levels and the retraining the first andsecond machine learning modules are performed in response to therequested track not indicated in the active cache list and indicated inthe inactive cache list.
 40. The system of claim 39, wherein the activeand inactive cache lists comprise least recently used (LRU) lists oftracks in the cache, wherein the first cache eviction algorithmcomprises an LRU algorithm that selects a track to evict that is at anLRU end of the LRU list, wherein the determining the modified firstconfidence level is based on a position of the requested track in theinactive cache list.
 41. The system of claim 40, wherein the determiningthe modified first confidence level based on the position of therequested track in the inactive cache list comprises: increasing thefirst confidence level in response to the requested track being closerto a most recently used (MRU) end of the inactive cache list than theLRU end; and decreasing the first confidence level in response to therequested track being closer to the LRU end than the MRU end.
 42. Thesystem of claim 40, wherein the determining the modified firstconfidence level based on the position of the requested track in theinactive cache list comprises: in response to the requested track beingcloser to a most recently used end of the inactive cache list than theLRU end, performing: calculating a positive margin of error comprising(the position of the requested track in the inactive cache list minus(an inactive cache list size divided by two)) divided by the inactivecache list size divided by two; and adjusting the first confidence levelby the positive margin of error to produce the modified first confidencelevel; in response to the requested track being closer to the LRU endthan a most recently used end of the inactive cache list, performing:calculating a negative margin of error comprising ((an inactive cachelist size divided by two) minus the position of the requested track inthe inactive cache list minus) divided by the inactive cache list sizedivided by two; and adjusting the first confidence level by the negativemargin of error to produce the modified first confidence level.
 43. Thesystem of claim 39, wherein the operations further comprise: providing afrequency counter for each of the tracks in the active and inactivecache lists indicating a number of times a track in the cache has beenaccessed while on the active cache list before being moved to theinactive cache list, wherein the second cache eviction algorithmcomprises a Least Frequently Used (LFU) algorithm that selects a trackto evict that has a lowest frequency counter of the frequency countersfor the tracks in the cache, wherein the determining the modified secondconfidence level is based on a frequency counter value of the requestedtrack with respect to an average of frequency counter values of tracksin the inactive cache list.
 44. The system of claim 43, wherein thedetermining the modified second confidence level based on the frequencycounter value of the requested track in the inactive cache listcomprises: increasing the second confidence level in response to thefrequency counter value of the requested track being greater than theaverage of the frequency counter values of the tracks in the inactivecache list; and decreasing the second confidence level in response tothe frequency counter value of the requested track being less than theaverage of the frequency counter values of the tracks in the inactivecache list.
 45. The system of claim 43, wherein the determining themodified second confidence level based on the frequency counter value ofthe requested track in the inactive cache list comprises: in response tothe frequency counter value of the requested track being greater thanthe average of the frequency counter values of the tracks in theinactive cache list, performing: calculating a positive margin of errorcomprising (the frequency count value of the requested track minus theaverage of the frequency counter values) divided by (a maximum frequencycounter value of frequency counter values in the inactive cache listminus the average of the frequency counter values); and adjusting thesecond confidence level by the positive margin of error to produce themodified second confidence level; in response to the frequency countervalue of the requested track being less than the average of thefrequency counter values of the tracks in the inactive cache list,performing: calculating a negative positive margin of error comprising(the average of the frequency counter values minus the frequency countvalue of the requested track) divided by (a maximum frequency countervalue of frequency counter values in the inactive cache list minus theaverage of the frequency counter values); and adjusting the secondconfidence level by the negative margin of error to produce the modifiedsecond confidence level.
 46. A method for demoting tracks from cache toa storage, comprising: providing a first machine learning module thatreceives as input first evict track to evict from the cache determinedby a first cache eviction algorithm and outputs a confidence levelindicating a likelihood the first cache eviction algorithm optimizes aread hit rate to the cache; providing a second machine learning modulethat receives as input a second evict track to evict from the cachedetermined by a second cache eviction algorithm and outputs a confidencelevel indicating a likelihood the second cache eviction algorithmoptimizes a read hit rate to the, wherein the first and second cacheeviction algorithms use different eviction schemes; determining amodified first confidence level based on first information on aspecified track; determining a modified second confidence level based onsecond information on the specified track; retraining the first machinelearning module to produce the modified first confidence level for thespecified track; and retraining the second machine learning module toproduce the modified second confidence level for the specified track.47. The method of claim 46, wherein the specified track comprises arequested track subject to a read request determined not to be in thecache, further comprising: maintaining an active cache list of thetracks in the cache; maintaining an inactive cache list of tracksevicted from the cache and removed from the active cache list; whereinthe determining the modified first and second confidence levels and theretraining the first and second machine learning modules are performedin response to the requested track not indicated in the active cachelist and indicated in the inactive cache list.
 48. The method of claim47, wherein the active and inactive cache lists comprise least recentlyused (LRU) lists of tracks in the cache, wherein the first cacheeviction algorithm comprises an LRU algorithm that selects a track toevict that is at an LRU end of the LRU list, wherein the determining themodified first confidence level is based on a position of the requestedtrack in the inactive cache list.
 49. The method of claim 48, whereinthe determining the modified first confidence level based on theposition of the requested track in the inactive cache list comprises:increasing the first confidence level in response to the requested trackbeing closer to a most recently used end (MRU) of the inactive cachelist than the LRU end; and decreasing the first confidence level inresponse to the requested track being closer to the LRU end than the MRUend.
 50. The method of claim 48, wherein the determining the modifiedfirst confidence level based on the position of the requested track inthe inactive cache list comprises: in response to the requested trackbeing closer to a most recently used end of the inactive cache list thanthe LRU end, performing: calculating a positive margin of errorcomprising (the position of the requested track in the inactive cachelist minus (an inactive cache list size divided by two)) divided by theinactive cache list size divided by two; and adjusting the firstconfidence level by the positive margin of error to produce the modifiedfirst confidence level; in response to the requested track being closerto the LRU end than a most recently used end of the inactive cache list,performing: calculating a negative margin of error comprising ((aninactive cache list size divided by two) minus the position of therequested track in the inactive cache list minus) divided by theinactive cache list size divided by two; and adjusting the firstconfidence level by the negative margin of error to produce the modifiedfirst confidence level.
 51. The method of claim 47, further comprising:providing a frequency counter for each of the tracks in the active andinactive cache lists indicating a number of times a track in the cachehas been accessed while on the active cache list before being moved tothe inactive cache list, wherein the second cache eviction algorithmcomprises a Least Frequently Used (LFU) algorithm that selects a trackto evict that has a lowest frequency counter of the frequency countersfor the tracks in the cache, wherein the determining the modified secondconfidence level is based on a frequency counter value of the requestedtrack with respect to an average of frequency counter values of tracksin the inactive cache list.
 52. The method of claim 51, wherein thedetermining the modified second confidence level based on the frequencycounter value of the requested track in the inactive cache listcomprises: increasing the second confidence level in response to thefrequency counter value of the requested track being greater than theaverage of the frequency counter values of the tracks in the inactivecache list; and decreasing the second confidence level in response tothe frequency counter value of the requested track being less than theaverage of the frequency counter values of the tracks in the inactivecache list.
 53. The method of claim 51, wherein the determining themodified second confidence level based on the frequency counter value ofthe requested track in the inactive cache list comprises: in response tothe frequency counter value of the requested track being greater thanthe average of the frequency counter values of the tracks in theinactive cache list, performing: calculating a positive margin of errorcomprising (the frequency count value of the requested track minus theaverage of the frequency counter values) divided by (a maximum frequencycounter value of frequency counter values in the inactive cache listminus the average of the frequency counter values); and adjusting thesecond confidence level by the positive margin of error to produce themodified second confidence level; in response to the frequency countervalue of the requested track being less than the average of thefrequency counter values of the tracks in the inactive cache list,performing: calculating a negative positive margin of error comprising(the average of the frequency counter values minus the frequency countvalue of the requested track) divided by (a maximum frequency countervalue of frequency counter values in the inactive cache list minus theaverage of the frequency counter values); and adjusting the secondconfidence level by the negative margin of error to produce the modifiedsecond confidence level.